Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds valuable behavioral context beyond annotations: it discloses that the tool probes each entity with ai_visibility_check, ranks results, and returns a ranked list including score, confidence, and signal density. This provides insight into the tool's internal operation without any contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loads the core purpose, and packs in mechanism, use case, and return format without redundancy. Every sentence contributes meaningful information, and it is concise for a tool of this complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description appropriately explains the return value (ranked list with score, confidence, signal density). Combined with full schema parameter descriptions and safety annotations, the description covers all essential aspects of the tool's behavior, use case, and outputs. No significant gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides full descriptions for all four parameters (100% coverage), including the semantic distinction of the first entity as the subject and the optional API key for Anthropic models. The description adds little beyond rephrasing 'entities' as 'your brand + competitors,' which is already captured in the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares AI visibility across multiple entities side-by-side, ranks them by score, and identifies the most/least recognized. It explicitly distinguishes itself from related tools by naming ai_visibility_check as the underlying probe and focusing on comparative ranking, making it highly specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a concrete use case (competitive AI-marketing audits) and an example query ('does Claude know about us as well as our competitors?'), which strongly implies when to use the tool. It does not explicitly exclude alternatives like compare_entities, but the context is clear enough for an agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.